貝蘇章Pei, Soo-Chang臺灣大學:電信工程學研究所李家宏Lee, Chia-HungChia-HungLee2010-07-012018-07-052010-07-012018-07-052009U0001-2206200911155400http://ntur.lib.ntu.edu.tw//handle/246246/188305當影像取樣的頻率太低時，影像就會發生混疊(aliasing)。傳統上，我們認為混疊是無用的並且使用抗混疊(anti-aliasing)濾波器將其消除。然而，這也消除了其中的資訊。事實上，混疊中也包含了影像高頻成分中的資訊，利用於超分辨率(super-resolution)的應用中。我們對同一個風景的一組影像擷取高頻資訊，並建立高解析度無混疊的影像。通常不同影像之間存在一些小位移，蘊含了對於風景些微不同的資訊。超分辨率影像重建可以被表示成一個偏移量未知的多頻道取樣問題。在這篇論文中，我們專注在運算這些偏移量，因為這是高解析度重建的必要條件。Vandewalle, Susstrunk和Vetterli提出了一個基於傅立葉轉換的頻域方法。一對影像的配準參數可以利用頻譜中沒有發生混疊的部分求得。然而，這個方法無法用在完全混疊的信號上。在這篇論文中，我們利用加伯轉換(Gabor transform)將其延伸到時頻域。理論上，我們的演算法會有更好的結果，因為頻域方法只是時頻域方法的一個特例。實驗的結果也的確證明了我們的方法在處理真實信號和影像時，的確有比較好的表現。Aliasing in images occurs when an image is sampled at a too low sampling rate. Conventionally, we consider aliasing useless and cancel it with an anti-aliasing filter. However, this also destroyed the information. In fact, aliasing also conveys useful information about the high frequency content of the image, which is exploited in super-resolution applications. We use a set of input images of the same scene to extract such high frequency information and create a higher resolution aliasing-free image. Typically, there is a small shift between the different images, such that they contain slightly different information about the scene. uper-resolution image reconstruction can be formulated as a multichannel sampling problem with unknown offsets. This thesis concentrates on the computation of these offsets, as they are an essential prerequisite for an accurate high resolution reconstruction. A frequency domain approach based on Fourier transform is proposed by Vandewalle, Susstrunk, and Vetterli. The registration parameters between a pair of signals are computed using the aliasing-free part of the spectrum. However, the method cannot work for totally-aliased signals. In this thesis, we extend the concept to the time-frequency domain, based on Gabor transform. Theoretically, our algorithm will perform better, since the frequency domain approach is simply a special case of the time-frequency domain approach. The experiment results show that the performance indeed increases when dealing with real signals and images.試委員會審定書 i謝 iii文摘要 vBSTRACT viiONTENTS ixIST OF FIGURES xiiiIST OF TABLES xvhapter 1 Introduction 1.1 Resolution 2.2 Super-resolution imaging 3.3 Aliasing 6.4 Applications 7.5 Thesis outline 9hapter 2 Problem Setup 11.1 Sampling methods 11.2 Multichannel sampling 14.3 Aliasing 16.4 Super-resolution imaging 19.4.1 Registration 20.4.2 Reconstruction 23.5 Conclusions 25hapter 3 Shift Estimation Algorithms 27.1 Frequency domain approach 27.1.1 Shift property of Fourier transform 28.1.2 Shift estimation 29.1.3 Limits of frequency domain approach 33.2 Time-Frequency domain approach 33.2.1 Typical time-frequency representations 34.2.2 Shift property of Gabor transform 39.2.3 Shift estimation 40.2.4 Weighting enhancement 43.3 Conclusions 44hapter 4 Experimental Results 47.1 Registration performance comparison 47.1.1 Artificial sinusoidal signal 48.1.2 Acoustic signal 54.1.3 Speech signal 60.2 Super-resolution imaging 66.2.1 Text 69.2.2 Ruler 72.2.3 Pillar 75.3 Conclusions 78hapter 5 Conclusions and Future Works 79.1 Thesis conclusions 79.2 Future works 81EFERENCE 832409278 bytesapplication/pdfen-US超分辨率配準混疊時頻加伯轉換Super-resolutionregistrationaliasingtime-frequencyGabor transformthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/188305/1/ntu-98-R96942050-1.pdf